1. Field of the Invention
This invention relates to digital signal processing. In particular, this invention relates to a system for detecting, in a channel of substantial signal interference, a digitally modulated signal with repetitively structured data.
2. Discussion of the Related Art
A time division multiplexed (TDM) signal has a characteristic repetitive structure in the modulated data resulting in part from markers which identify the boundaries of data words and frames. The structural organization of the TDM signal permits the signal to carry data combined from different sources ("tributary data streams") to be transmitted as a single composite signal. Periodicity in a TDM signal can also arise from the inherent nature of time-multiplexing tributary data streams. If a tributary data stream is self-correlated, the resulting composite signal is periodically correlated.
In the prior art, digitally modulated signals are typically detected by examining a spectrum ("detector spectrum") of an input signal to determine the signal's symbol rate, frame rate, word rate, and center frequency. A typical detection method filters the input signal to eliminate noise, applies a quadratic operator to the filtered signal, and then computes the detector spectrum from the quadratic operator's signal output. The detector spectrum is then analyzed for peaks at specific frequency locations.
In another class of methods, a digitally modulated signal is detected using "cyclostationary signal processing" techniques. In these methods, a cyclic correlation, a cyclic spectrum, or both, are computed. The computed cyclic correlation value, or the cyclic spectrum, is analyzed for peaks corresponding to the symbol rate, the center frequency, the frame rate, and the word rate of the signal.
The detector spectrum resulting from any of the above methods can yield an erroneous detection result when interference is present. Such an erroneous detection results from the quadratic operations, which are inherent in these methods, generating spurious peaks from the cross products of interfering signals, thereby obliterating from the detector spectrum the peaks of the signal to be detected. In fact, peaks resulting from signal interference can dominate the detector spectrum.
Several approaches to improve detection of structured signals from a detector spectrum have been tried in the prior art. For example, one method "rasters" the detector spectrum at the expected frame rate and searches for an alignment of peaks to confirm periodicity at the frame rate. Rastering divides the spectrum into sections of equal frequency intervals, e.g. a frequency interval corresponding to an integer multiple of the frame rate, and aligns these equal interval sections of the spectrum with each other. However, the prior art approaches are successful only when the interfering signals are weak. When the interfering signals are strong, the numerous peaks present in the rastered spectrum render a determination of whether the spectral peaks are aligned difficult. To improve detection of a peak alignment, the operator varies a threshold and displays only the peak amplitudes in the spectrum exceeding the threshold. However, the proper selection of an appropriate threshold is difficult because the relative power levels between the signal and the interfering signal or signals are unknown. Thus, such an approach fails to detect the signal when the threshold is incorrectly set.
Other approaches to improve signal detection include using filters on the input signal. A band pass filter can be used to attenuate interference from signals located in frequencies outside the pass band. Also, an adaptive line-canceling filter or a notch filter can be used to attenuate narrow-band interference collocated in the frequencies of the signal to be detected. However, difficulties exist in attenuating wide-band interfering signals collocated in the frequencies of the signal to be detected.
Even without the presence of an overriding signal interference, human intervention is still required to establish a signal detection. The required human intervention prevents automated spectrum monitoring.